Q: In your work with text analytics, what behavior or outcome do your models predict?

A: First of all, I’m working in the insurance world. As of now, our main focus had been to identify actionable topics in the free format text fields where our exclusive agents can write notes on everything they want about our clients. For instance, the most useful information we try to identify is the “life events” (newborn, wedding, new car, new home, new job, etc.) of our clients.

Q: How does text analytics deliver value at your organization – what is one specific way in which it actively drives decisions or operations?

A: We are at a very early stage of using text analytics in the organization. For this moment, only research prototypes had been delivered. By the end of 2016, we will use text analytics to prioritize which clients to contact first, mostly for cross sell opportunities.

Q: Can you describe a quantitative result, such as the predictive lift of your model or the ROI of an analytics initiative?

A: It is really difficult to articulate for this moment, but early tests in a recent marketing campaign showed an improvement by 15% on our cross sell acceptance ratio compared to a control group.

Q: What surprising discovery or insight have you unearthed in your data?

A: Hey, this was something we were really not looking for, but we discovered accidentally that a lot of our clients are annoyed by the fact that we have really strict underwriting rules for oil tanks older than 5 years. We are losing a lot of clients because of this rule, while 5 years of age is not necessarily too old for an oil tank.

Q: Sneak preview: Please tell us a take-away that you will provide during your talk at Text Analytics World.

A: I will try to demonstrate that hands on efforts can have a great value while doing text analytics, especially in an industry heavily relaying in specialized terminology.